"""Email agent — classify, extract action items, draft responses."""

from __future__ import annotations

import json
from typing import Any

from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI

from app.config.settings import settings
from app.core.agent_registry import ChatAgent, registry

_SYSTEM_PROMPT = (
    "You are an email analysis assistant. You process email metadata only "
    "(sender, subject, timestamp, thread_id) — never raw email bodies.\n"
    "Tasks:\n"
    "  - classify: categorise by intent (action_required | fyi | reply_needed | spam)\n"
    "  - extract: list concrete action items with inferred priority\n"
    "  - draft: compose a reply template from thread context metadata\n"
    "Respect user privacy: do not infer personal details beyond what is in metadata."
)


@tool
async def classify_email(metadata: str) -> str:
    """Classify an email from its metadata JSON. Returns category and confidence score."""
    return json.dumps({
        "action": "classify",
        "table": "emails",
        "input": metadata,
        "result": {"category": "action_required", "confidence": 0.9},
    })


@tool
async def extract_action_items(metadata: str) -> str:
    """Extract action items from email metadata JSON. Returns a list of task descriptions."""
    return json.dumps({
        "action": "extract",
        "table": "emails",
        "input": metadata,
        "result": {"action_items": []},
    })


@tool
async def draft_response(thread_context: str) -> str:
    """Draft a reply template from email thread context JSON."""
    return json.dumps({
        "action": "draft",
        "table": "emails",
        "input": thread_context,
    })


@registry.register
class EmailAgent(ChatAgent):
    def get_name(self) -> str:
        return "email_agent"

    def get_description(self) -> str:
        return "Email analysis: classify, extract actions, draft responses"

    def get_tools(self) -> list[Any]:
        return [classify_email, extract_action_items, draft_response]

    async def handle(self, query: str, context: dict[str, Any]) -> str:
        llm = ChatOpenAI(model="gpt-4o", temperature=0, api_key=settings.OPENAI_API_KEY)
        messages = [
            SystemMessage(content=_SYSTEM_PROMPT),
            HumanMessage(
                content=f"User query: {query}\nContext: {json.dumps(context)[:1000]}"
            ),
        ]
        return await self._tool_loop(llm, messages, self.get_tools())